

import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
    from scipy.stats import pearsonr, spearmanr
    from sklearn.metrics import fa_score, matthews_corrcoef


__A			:					str						=			(
    'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
    'library. You can have a look at this example script for pointers: '
    'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)





def 	__a     (      A__			:  Optional[int]			,  A__			:  Optional[int]       ):
    warnings.warn(A_			,  A_       )
    requires_backends(A_			,  "sklearn"       )
    return (preds == labels).mean()





def 	__a     (      A__			:  Any			,  A__			:  Dict       ):
    warnings.warn(A_			,  A_       )
    requires_backends(A_			,  "sklearn"       )
    SCREAMING_SNAKE_CASE										=   simple_accuracy(A_			,  A_       )
    SCREAMING_SNAKE_CASE										=   fa_score(y_true=A_			,  y_pred=A_       )
    return {
        "acc": acc,
        "f1": fa,
        "acc_and_f1": (acc + fa) / 2,
    }





def 	__a     (      A__			:  int			,  A__			:  Optional[Any]       ):
    warnings.warn(A_			,  A_       )
    requires_backends(A_			,  "sklearn"       )
    SCREAMING_SNAKE_CASE										=   pearsonr(A_			,  A_       )[0]
    SCREAMING_SNAKE_CASE										=   spearmanr(A_			,  A_       )[0]
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
        "corr": (pearson_corr + spearman_corr) / 2,
    }





def 	__a     (      A__			:  Any			,  A__			:  List[Any]			,  A__			:  Any       ):
    warnings.warn(A_			,  A_       )
    requires_backends(A_			,  "sklearn"       )
    assert len(A_       ) == len(A_       ), F"Predictions and labels have mismatched lengths {len(A_       )} and {len(A_       )}"
    if task_name == "cola":
        return {"mcc": matthews_corrcoef(A_			,  A_       )}
    elif task_name == "sst-2":
        return {"acc": simple_accuracy(A_			,  A_       )}
    elif task_name == "mrpc":
        return acc_and_fa(A_			,  A_       )
    elif task_name == "sts-b":
        return pearson_and_spearman(A_			,  A_       )
    elif task_name == "qqp":
        return acc_and_fa(A_			,  A_       )
    elif task_name == "mnli":
        return {"mnli/acc": simple_accuracy(A_			,  A_       )}
    elif task_name == "mnli-mm":
        return {"mnli-mm/acc": simple_accuracy(A_			,  A_       )}
    elif task_name == "qnli":
        return {"acc": simple_accuracy(A_			,  A_       )}
    elif task_name == "rte":
        return {"acc": simple_accuracy(A_			,  A_       )}
    elif task_name == "wnli":
        return {"acc": simple_accuracy(A_			,  A_       )}
    elif task_name == "hans":
        return {"acc": simple_accuracy(A_			,  A_       )}
    else:
        raise KeyError(A_       )





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Optional[Any]			,  A__			:  Optional[Any]       ):
    warnings.warn(A_			,  A_       )
    requires_backends(A_			,  "sklearn"       )
    if len(A_       ) != len(A_       ):
        raise ValueError(F"Predictions and labels have mismatched lengths {len(A_       )} and {len(A_       )}"       )
    if task_name == "xnli":
        return {"acc": simple_accuracy(A_			,  A_       )}
    else:
        raise KeyError(A_       )

from __future__ import annotations

def a__       (			A_,	A_   ):






  '''simple docstring'''





  if b == 0:
    return (1, 0)
  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	a % b   )
  __magic_name__								=			a // b
  return (y, x - k * y)

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	A_   )
  __magic_name__								=			na * na
  __magic_name__								=			ra * x * na + ra * y * na
  return (n % m + m) % m

def a__       (			A_,	A_   ):






  '''simple docstring'''





  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	A_   )
  if b < 0:
    __magic_name__								=			(b % n + n) % n
  return b

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__	,       __magic_name__								=			invert_modulo(A_,	A_   ), invert_modulo(A_,	A_   )
  __magic_name__								=			na * na
  __magic_name__								=			ra * x * na + ra * y * na
  return (n % m + m) % m


if __name__ == "__main__":
    from doctest import testmod

    testmod(name='chinese_remainder_theorem', verbose=True)
    testmod(name='chinese_remainder_theorem2', verbose=True)
    testmod(name='invert_modulo', verbose=True)
    testmod(name='extended_euclid', verbose=True)

